def handle_file(filename, output_file, sw_map_file):
reader = NTriplesReader()
out = MsgpackStreamWriter(output_file)
map_out = NTriplesWriter(sw_map_file)
for line in open(filename, 'rb'):
if not line.startswith(b'#'):
handle_triple(line.decode('utf-8').strip(), reader, out, map_out)
def run_wordnet(input_dir, output_file, sw_map_file):
out = MsgpackStreamWriter(output_file)
map_out = NTriplesWriter(sw_map_file)
reader = NTriplesReader()
synset_senses = defaultdict(list)
sense_synsets = {}
labels = {}
glossary = {}
concept_map = {}
sense_to_synset = {}
# Parse lines such as:
# wn30:synset-Aeolian-noun-2 rdfs:label "Aeolian"@en-us .
for subj, rel, obj, objtag in reader.parse_file(os.path.join(input_dir, 'wordnet-synset.ttl')):
if resource_name(rel) == 'label':
# Everything in WordNet is in English
assert objtag == 'en'
labels[subj] = obj
for subj, rel, obj, objtag in reader.parse_file(os.path.join(input_dir, 'wordnet-glossary.ttl')):
if resource_name(rel) == 'gloss':
assert objtag == 'en'
# Take the definition up to the first semicolon
text = obj.split(';')[0]
# Remove introductory phrases with a colon
text = text.split(': ', 1)[-1]
# Remove parenthesized expressions
while True:
newtext = re.sub(r'\(.+?\) ?', '', text).strip()
if newtext == text or newtext == '':
break
else:
text = newtext
glossary[subj] = text.replace('/', '_')
# Get the list of word senses in each synset, and make a bidirectional mapping.
#
# Example line:
# wn30:synset-Aeolian-noun-2 wn20schema:containsWordSense wn30:wordsense-Aeolian-noun-2 .
for subj, rel, obj, objtag in reader.parse_file(os.path.join(input_dir, 'full/wordnet-wordsense-synset-relations.ttl')):
if resource_name(rel) == 'containsWordSense':
synset_senses[subj].append(obj)
sense_synsets[obj] = subj
# Assign every synset to a disambiguated concept.
for synset in synset_senses:
synset_name = labels[synset]
synset_pos = synset.split('-')[-2]
pos = PARTS_OF_SPEECH[synset_pos]
disambig = glossary[synset]
concept = standardized_concept_uri('en', synset_name, pos, disambig)
concept_map[synset] = concept
# Map senses to their synsets.
for sense, synset in sense_synsets.items():
sense_to_synset[sense] = synset
for filename in (
'wordnet-attribute.ttl', 'wordnet-causes.ttl',
'wordnet-classifiedby.ttl', 'wordnet-entailment.ttl',
'wordnet-hyponym.ttl', 'wordnet-instances.ttl',
'wordnet-membermeronym.ttl', 'wordnet-partmeronym.ttl',
'wordnet-sameverbgroupas.ttl', 'wordnet-similarity.ttl',
'wordnet-substancemeronym.ttl', 'full/wordnet-antonym.ttl',
'full/wordnet-derivationallyrelated.ttl',
'full/wordnet-participleof.ttl',
'full/wordnet-pertainsto.ttl',
'full/wordnet-seealso.ttl'
):
filepath = os.path.join(input_dir, filename)
if os.path.exists(filepath):
for web_subj, web_rel, web_obj, objtag in reader.parse_file(filepath):
# If this relation involves word senses, map them to their synsets
# first.
if web_subj in sense_to_synset:
web_subj = sense_to_synset[web_subj]
if web_obj in sense_to_synset:
web_obj = sense_to_synset[web_obj]
subj = concept_map[web_subj]
obj = concept_map[web_obj]
pred_label = resource_name(web_rel)
if pred_label in REL_MAPPING:
mapped_rel = REL_MAPPING[pred_label]
# Handle WordNet relations that are the reverse of ConceptNet
# relations. Change the word 'meronym' to 'holonym' if
# necessary.
if mapped_rel.startswith('~'):
subj, obj = obj, subj
web_subj, web_obj = web_obj, web_subj
web_rel = web_rel.replace('meronym', 'holonym')
mapped_rel = mapped_rel[1:]
rel = join_uri('r', mapped_rel)
else:
rel = join_uri('r', 'wordnet', pred_label)
map_out.write_link(web_rel, full_conceptnet_url(rel))
map_out.write_link(web_subj, full_conceptnet_url(subj))
map_out.write_link(web_obj, full_conceptnet_url(obj))
edge = make_edge(
rel, subj, obj, dataset='/d/wordnet/3.0',
license='/l/CC/By', sources=SOURCE, weight=2.0
)
out.write(edge)
def handle_file(filename, output_file, sw_map_file):
reader = NTriplesReader()
out = JSONStreamWriter(output_file)
map_out = NTriplesWriter(sw_map_file)
for line in open(filename, 'rb'):
handle_triple(line.decode('utf-8').strip(), reader, out, map_out)
def run_wordnet(input_dir, output_file, sw_map_file):
out = MsgpackStreamWriter(output_file)
map_out = NTriplesWriter(sw_map_file)
reader = NTriplesReader()
synset_senses = defaultdict(list)
sense_synsets = {}
labels = {}
glossary = {}
concept_map = {}
sense_to_synset = {}
# Parse lines such as:
# wn30:synset-Aeolian-noun-2 rdfs:label "Aeolian"@en-us .
for subj, rel, obj, objtag in reader.parse_file(
os.path.join(input_dir, 'wordnet-synset.ttl')):
if resource_name(rel) == 'label':
# Everything in WordNet is in English
assert objtag == 'en'
labels[subj] = obj
for subj, rel, obj, objtag in reader.parse_file(
os.path.join(input_dir, 'wordnet-glossary.ttl')):
if resource_name(rel) == 'gloss':
assert objtag == 'en'
# Take the definition up to the first semicolon
text = obj.split(';')[0]
# Remove introductory phrases with a colon
text = text.split(': ', 1)[-1]
# Remove parenthesized expressions
while True:
newtext = re.sub(r'\(.+?\) ?', '', text).strip()
if newtext == text or newtext == '':
break
else:
text = newtext
glossary[subj] = text.replace('/', '_')
# Get the list of word senses in each synset, and make a bidirectional mapping.
#
# Example line:
# wn30:synset-Aeolian-noun-2 wn20schema:containsWordSense wn30:wordsense-Aeolian-noun-2 .
for subj, rel, obj, objtag in reader.parse_file(
os.path.join(input_dir,
'full/wordnet-wordsense-synset-relations.ttl')):
if resource_name(rel) == 'containsWordSense':
synset_senses[subj].append(obj)
sense_synsets[obj] = subj
# Assign every synset to a disambiguated concept.
for synset in synset_senses:
synset_name = labels[synset]
synset_pos = synset.split('-')[-2]
pos = PARTS_OF_SPEECH[synset_pos]
disambig = glossary[synset]
concept = standardized_concept_uri('en', synset_name, pos, disambig)
concept_map[synset] = concept
# Map senses to their synsets.
for sense, synset in sense_synsets.items():
sense_to_synset[sense] = synset
for filename in ('wordnet-attribute.ttl', 'wordnet-causes.ttl',
'wordnet-classifiedby.ttl', 'wordnet-entailment.ttl',
'wordnet-hyponym.ttl', 'wordnet-instances.ttl',
'wordnet-membermeronym.ttl', 'wordnet-partmeronym.ttl',
'wordnet-sameverbgroupas.ttl', 'wordnet-similarity.ttl',
'wordnet-substancemeronym.ttl',
'full/wordnet-antonym.ttl',
'full/wordnet-derivationallyrelated.ttl',
'full/wordnet-participleof.ttl',
'full/wordnet-pertainsto.ttl',
'full/wordnet-seealso.ttl'):
filepath = os.path.join(input_dir, filename)
if os.path.exists(filepath):
for web_subj, web_rel, web_obj, objtag in reader.parse_file(
filepath):
# If this relation involves word senses, map them to their synsets
# first.
if web_subj in sense_to_synset:
web_subj = sense_to_synset[web_subj]
if web_obj in sense_to_synset:
web_obj = sense_to_synset[web_obj]
subj = concept_map[web_subj]
obj = concept_map[web_obj]
pred_label = resource_name(web_rel)
if pred_label in REL_MAPPING:
mapped_rel = REL_MAPPING[pred_label]
# Handle WordNet relations that are the reverse of ConceptNet
# relations. Change the word 'meronym' to 'holonym' if
# necessary.
if mapped_rel.startswith('~'):
subj, obj = obj, subj
web_subj, web_obj = web_obj, web_subj
web_rel = web_rel.replace('meronym', 'holonym')
mapped_rel = mapped_rel[1:]
rel = join_uri('r', mapped_rel)
else:
rel = join_uri('r', 'wordnet', pred_label)
map_out.write_link(web_rel, full_conceptnet_url(rel))
map_out.write_link(web_subj, full_conceptnet_url(subj))
map_out.write_link(web_obj, full_conceptnet_url(obj))
edge = make_edge(rel,
subj,
obj,
dataset='/d/wordnet/3.0',
license='/l/CC/By',
sources=SOURCE,
weight=2.0)
out.write(edge)
def run_umbel(input_dir, output_file, sw_map_file):
"""
Read N-Triples files containing Umbel data, outputting a file of
ConceptNet edges and a file of mappings between the Semantic Web and
ConceptNet.
"""
out = MsgpackStreamWriter(output_file)
map_out = NTriplesWriter(sw_map_file)
reader = NTriplesReader()
labels = {}
label_sets = defaultdict(set)
# There are two files we want to parse:
# - umbel.nt, a transformation of umbel.n3, which is available from
# https://github.com/structureddynamics/UMBEL/.
# - umbel_links.nt, distributed with DBPedia 3.9.
#
# We parse them both in this file so that umbel_links can reuse the
# concept names extracted from umbel.nt.
main_file = os.path.join(input_dir, 'umbel.nt')
dbpedia_link_file = os.path.join(input_dir, 'umbel_links.nt')
# Read through umbel.nt once, finding all the "preferred labels". We will
# use these as the surface texts for the nodes.
for web_subj, web_rel, web_obj, objtag in reader.parse_file(main_file):
if resource_name(web_rel) == 'prefLabel':
# 'CW' and 'PCW' are Cyc jargon for 'conceptual works'. If a node
# cannot be described except as a CW, we're probably not
# interested in it.
if 'CW' not in web_obj.split() and 'PCW' not in web_obj.split():
labels[web_subj] = web_obj
if resource_name(web_rel).endswith('Label'):
text = standardize_text(web_obj)
label_sets[text].add(web_subj)
# Read through umbel.nt again and extract ConceptNet edges.
for web_subj, web_rel, web_obj, objtag in reader.parse_file(main_file):
if objtag == 'URL' and acceptable_node(web_obj) and acceptable_node(
web_subj):
# Only use nodes for which we've seen preferred labels.
# (This skips some anonymous OWL-cruft nodes.)
if web_subj in labels and web_obj in labels:
subj_uri = standardized_concept_uri('en', labels[web_subj])
obj_uri = standardized_concept_uri('en', labels[web_obj])
rel_name = resource_name(web_rel)
# Check if this is a relation we want to handle.
if rel_name in REL_MAPPING:
# Write the ConceptNet edges and the mappings to Semantic Web URLs.
rel_uri, frame = REL_MAPPING[rel_name]
surface = frame % (labels[web_subj], labels[web_obj])
out.write(
umbel_edge(rel_uri, subj_uri, obj_uri, surface,
SOURCE))
map_out.write_link(web_rel, full_conceptnet_url(rel_uri))
map_out.write_link(web_subj, full_conceptnet_url(subj_uri))
map_out.write_link(web_obj, full_conceptnet_url(obj_uri))
# altLabel relations assign different texts to the same node. We'll
# represent those in ConceptNet with Synonym relations.
elif web_rel.endswith('altLabel'):
# Make sure we know what's being labeled.
if web_subj in labels:
name = web_obj
words = name.split(' ')
if standardized_concept_name(
'en', name) != standardized_concept_name(
'en', labels[web_subj]):
if not set(words) & IGNORED_WORDS:
main_label = standardized_concept_uri(
'en', labels[web_subj])
name_text = standardize_text(name)
if len(label_sets[name_text]) >= 2 or len(
name_text) <= 3:
disambig = un_camel_case(resource_name(web_subj))
# Cyc does not distinguish texts by their part of speech, so use
# '_' as the part of speech symbol.
alt_label = standardized_concept_uri(
'en', name, '_', disambig)
else:
alt_label = standardized_concept_uri('en', name)
surface = SYN_FRAME % (name, labels[web_subj])
out.write(
umbel_edge('/r/Synonym', alt_label, main_label,
surface, SOURCE))
for web_subj, web_rel, web_obj, objtag in reader.parse_file(
dbpedia_link_file):
if objtag == 'URL' and acceptable_node(web_obj) and acceptable_node(
web_subj):
if web_obj in labels:
subj_label = resource_name(web_subj).replace('_', ' ')
subj_uri = translate_dbpedia_url(web_subj)
obj_label = labels[web_obj]
obj_uri = standardized_concept_uri('en', obj_label)
rel_name = resource_name(web_rel)
if rel_name in REL_MAPPING:
rel_uri, frame = REL_MAPPING[rel_name]
surface = frame % (subj_label, obj_label)
out.write(
umbel_edge(rel_uri, subj_uri, obj_uri, surface,
LINK_SOURCE))
map_out.write_link(web_rel, full_conceptnet_url(rel_uri))
map_out.write_link(web_subj, full_conceptnet_url(subj_uri))
map_out.write_link(web_obj, full_conceptnet_url(obj_uri))
def run_umbel(input_dir, output_file, sw_map_file):
"""
Read N-Triples files containing Umbel data, outputting a file of
ConceptNet edges and a file of mappings between the Semantic Web and
ConceptNet.
"""
out = MsgpackStreamWriter(output_file)
map_out = NTriplesWriter(sw_map_file)
reader = NTriplesReader()
labels = {}
label_sets = defaultdict(set)
# There are two files we want to parse:
# - umbel.nt, a transformation of umbel.n3, which is available from
# https://github.com/structureddynamics/UMBEL/.
# - umbel_links.nt, distributed with DBPedia 3.9.
#
# We parse them both in this file so that umbel_links can reuse the
# concept names extracted from umbel.nt.
main_file = os.path.join(input_dir, 'umbel.nt')
dbpedia_link_file = os.path.join(input_dir, 'umbel_links.nt')
# Read through umbel.nt once, finding all the "preferred labels". We will
# use these as the surface texts for the nodes.
for web_subj, web_rel, web_obj, objtag in reader.parse_file(main_file):
if resource_name(web_rel) == 'prefLabel':
# 'CW' and 'PCW' are Cyc jargon for 'conceptual works'. If a node
# cannot be described except as a CW, we're probably not
# interested in it.
if 'CW' not in web_obj.split() and 'PCW' not in web_obj.split():
labels[web_subj] = web_obj
if resource_name(web_rel).endswith('Label'):
text = standardize_text(web_obj)
label_sets[text].add(web_subj)
# Read through umbel.nt again and extract ConceptNet edges.
for web_subj, web_rel, web_obj, objtag in reader.parse_file(main_file):
if objtag == 'URL' and acceptable_node(web_obj) and acceptable_node(web_subj):
# Only use nodes for which we've seen preferred labels.
# (This skips some anonymous OWL-cruft nodes.)
if web_subj in labels and web_obj in labels:
subj_uri = standardized_concept_uri('en', labels[web_subj])
obj_uri = standardized_concept_uri('en', labels[web_obj])
rel_name = resource_name(web_rel)
# Check if this is a relation we want to handle.
if rel_name in REL_MAPPING:
# Write the ConceptNet edges and the mappings to Semantic Web URLs.
rel_uri, frame = REL_MAPPING[rel_name]
surface = frame % (labels[web_subj], labels[web_obj])
out.write(umbel_edge(rel_uri, subj_uri, obj_uri, surface, SOURCE))
map_out.write_link(web_rel, full_conceptnet_url(rel_uri))
map_out.write_link(web_subj, full_conceptnet_url(subj_uri))
map_out.write_link(web_obj, full_conceptnet_url(obj_uri))
# altLabel relations assign different texts to the same node. We'll
# represent those in ConceptNet with Synonym relations.
elif web_rel.endswith('altLabel'):
# Make sure we know what's being labeled.
if web_subj in labels:
name = web_obj
words = name.split(' ')
if standardized_concept_name('en', name) != standardized_concept_name('en', labels[web_subj]):
if not set(words) & IGNORED_WORDS:
main_label = standardized_concept_uri('en', labels[web_subj])
name_text = standardize_text(name)
if len(label_sets[name_text]) >= 2 or len(name_text) <= 3:
disambig = un_camel_case(resource_name(web_subj))
# Cyc does not distinguish texts by their part of speech, so use
# '_' as the part of speech symbol.
alt_label = standardized_concept_uri('en', name, '_', disambig)
else:
alt_label = standardized_concept_uri('en', name)
surface = SYN_FRAME % (name, labels[web_subj])
out.write(umbel_edge('/r/Synonym', alt_label, main_label, surface, SOURCE))
for web_subj, web_rel, web_obj, objtag in reader.parse_file(dbpedia_link_file):
if objtag == 'URL' and acceptable_node(web_obj) and acceptable_node(web_subj):
if web_obj in labels:
subj_label = resource_name(web_subj).replace('_', ' ')
subj_uri = translate_dbpedia_url(web_subj)
obj_label = labels[web_obj]
obj_uri = standardized_concept_uri('en', obj_label)
rel_name = resource_name(web_rel)
if rel_name in REL_MAPPING:
rel_uri, frame = REL_MAPPING[rel_name]
surface = frame % (subj_label, obj_label)
out.write(umbel_edge(rel_uri, subj_uri, obj_uri, surface, LINK_SOURCE))
map_out.write_link(web_rel, full_conceptnet_url(rel_uri))
map_out.write_link(web_subj, full_conceptnet_url(subj_uri))
map_out.write_link(web_obj, full_conceptnet_url(obj_uri))
